Abstract
We propose a novel, end-to-end, decision support system, to facilitate ICU clinicians to identify ventilatory over-assistance and titrate the level of respiratory support. Our method consists of a wavelet-based algorithm to automatically segment distinct breaths in mechanical ventilator respiratory recordings, and a 1D CNN schema for the classification of new respirations. A dataset of 40 respiratory recordings, taken from 38 ICU patients, was used for quantitative performance assessment, where our approach achieved impressive results in detecting ventilatory over-assistance in a total of 76,595 distinct breath patterns. The proposed system is non-invasive, requires no changes in clinical practice and is readily applicable to contemporary mechanical ventilators. Accordingly, it facilitates efficient ventilator exploitation and reduces the risk of mechanical ventilation complications.
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References
Akoumianaki, E., et al.: The application of esophageal pressure measurement in patients with respiratory failure. Am. J. Respir. Crit. Care Med. 189(5), 520–531 (2014)
Albani, F., et al.: Flow index accurately identifies breaths with low or high inspiratory effort during pressure support ventilation. Crit. Care 25(1), 1–11 (2021)
Dres, M., Goligher, E.C., Heunks, L.M., Brochard, L.J.: Critical illness-associated diaphragm weakness. Intensive Care Med. 43(10), 1441–1452 (2017). https://doi.org/10.1007/s00134-017-4928-4
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Discov. 33(4), 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1
Geurts, P.: Pattern extraction for time series classification. In: De Raedt, L., Siebes, A. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 115–127. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44794-6_10
Goligher, E.C., et al.: Mechanical ventilation-induced diaphragm atrophy strongly impacts clinical outcomes. Am. J. Respir. Crit. Care Med. 197(2), 204–213 (2018)
Ilyas, I.F., Chu, X.: Data Cleaning. Morgan & Claypool (2019)
Kaczka, D., Dellacá, R.L.: Oscillation mechanics of the respiratory system: applications to lung disease. Crit. Rev. Biomed. Eng. 39(4) (2011)
Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455–5516 (2020). https://doi.org/10.1007/s10462-020-09825-6
Kiranyaz, S., et al.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)
Lassola, S., et al.: Central venous pressure swing outperforms diaphragm ultrasound as a measure of inspiratory effort during pressure support ventilation in COVID-19 patients. J. Clin. Monit. Comput., 1–11 (2021)
Lopez-Meyer, P., Sazonov, E.: Automatic breathing segmentation from wearable respiration sensors. In: Fifth International Conference on Sensing Technology. IEEE (2011)
Noto, T., Zhou, G., Schuele, S., Templer, J., Zelano, C.: Automated analysis of breathing waveforms using breathmetrics: a respiratory signal processing toolbox. Chem. Senses 43(8), 583–597 (2018)
Ranieri, M.V., et al.: Effects of proportional assist ventilation on inspiratory muscle effort in patients with chronic obstructive pulmonary disease and acute respiratory failure. J. Am. Soc. Anesthesiol. 86(1), 79–91 (1997)
Sassoon, C.S.: Triggering of the ventilator in patient-ventilator interactions. Respir. Care 56(1), 39–51 (2011)
Smirnov, D., Nguifo, E.M.: Time series classification with recurrent neural networks. In: Advanced Analytics and Learning on Temporal Data 8 (2018)
Spadaro, S., et al.: Impact of prolonged assisted ventilation on diaphragmatic efficiency: NAVA versus PSV. Crit. Care 20(1), 1–12 (2015)
Telias, I., et al.: Airway occlusion pressure as an estimate of respiratory drive and inspiratory effort during assisted ventilation. Am. J. Respir. Crit. Care Med. 201(9), 1086–1098 (2020)
Umbrello, M., et al.: Oesophageal pressure and respiratory muscle ultrasonographic measurements indicate inspiratory effort during pressure support ventilation. Br. J. Anaesth. 125(1), e148–e157 (2020)
Vaporidi, K.: NAVA and PAV+ for lung and diaphragm protection. Curr. Opin. Crit. Care 26(1), 41–46 (2020)
Vaporidi, K., et al.: Esophageal and transdiaphragmatic pressure swings as indices of inspiratory effort. Respir. Physiol. Neurobiol. 284, 103561 (2021)
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Sylligardos, E., Sigalas, M., Soundoulounaki, S., Vaporidi, K., Trahanias, P. (2022). A Deep Learning Approach to Detect Ventilatory Over-Assistance. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13364. Springer, Cham. https://doi.org/10.1007/978-3-031-09282-4_42
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DOI: https://doi.org/10.1007/978-3-031-09282-4_42
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